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洞見 - Open-World Object Detection
SKDF: A Simple Knowledge Distillation Framework for Distilling Open-Vocabulary Knowledge to Open-World Object Detector
The core message of this paper is that a simple knowledge distillation approach can effectively transfer the open-world knowledge from a large pre-trained vision-language model to a specialized open-world object detector, achieving better performance for unknown object detection compared to the teacher model.
YOLOOC: A YOLO-based Open-Class Incremental Object Detector with Novel Class Discovery
The core message of this paper is to propose a new YOLO-based open-class incremental object detection framework, YOLOOC, that can effectively discover novel classes during inference while maintaining the performance on previously known classes.
